1. Introduction
Energy is the need of the modern world, but its conventional sources are depleting with each passing day, which includes thermal, nuclear, and natural gas [
1,
2,
3]. These sources are insufficient and not environment friendly [
4,
5]. Therefore, it is becoming imperative to find alternate sources of energy that can meet the requirements of energy in the future and should be environmentally friendly [
6]. High prices of electrical energy from thermal power plants and intermittency of renewable energy sources [
2] move extra emphasis towards hydro-thermal scheduling [
7]. Additionally, owing to the continuous diminishing of conventional sources [
1,
2], the active power generation and the control and compensation of reactive power are becoming the focus for the economic dispatch [
8,
9,
10,
11,
12], and researchers have also found the way to optimally generate, shed, and forecast the electrical power [
13,
14,
15]. Recently, solar energy has become one of the free, clean, and reliable sources of energy [
3,
16].
Neeraj et al. employ hybrid neural network and fuzzy-logic control for maximum power point tracking (MPPT) of photovoltaic (PV) [
17], while Gul et al. use a fuzzy controller that depends on a distinct combination of inputs and outputs to track MPPT [
18]. Ref. [
19], ant colony optimization (ACO) algorithm with MPPT is used in case of the hybrid PV-wind system to produce power for rural areas. Moreover, an advanced MPPT method considering temperature variability for a PV system to attain maximum tracking performance is designed by [
20]. Therefore, with the passage of time, various MPPT techniques such as incremental conductance (IC), perturb and observe (P&O), and artificial neural networks (ANN) are developed to ensure maximum gain from solar modules [
21,
22,
23]. Furthermore, in Ref. [
24], the center of inertia technique is used to evaluate the performance of an electronic inverter-based PV power system. However, these techniques have shortcomings including an inability to track continuous power and oscillations near the maximum point. Furthermore, conventional techniques are not able to detect the maximum power point accurately when weather conditions change rapidly [
25] and computational time is relatively long to calculate the maximum power point. Usually, single switch buck-boost converters are used with these conventional techniques [
26]. These converters do not provide the isolation between input and output sides and a high voltage conversion ratio. From the system point of view and utilization of AC loads, the conventional converters produce DC voltages from the PV panels and then invert it into AC through the open loop inverters. These inverters present high total harmonic distortion (THD) [
27,
28,
29,
30]. Therefore, an active, computationally fast, resilient, and efficient MPPT algorithm is required.
In this paper, a fuzzy logic controller is employed for MPPT to overcome the aforementioned shortcomings by tracking the maximum power point (MPP) in real time. Fuzzy logic based control offers an advantage in that it does not oscillate near the MPP [
31,
32]. This kind of control is unique for push pull current-fed boost converters where the high frequency transformer is used to provide the galvanic isolation between the input and output side along with a high conversion ratio [
33,
34]. DC voltage is inverted to AC through a voltage source inverter (VSI) with a fuzzy logic closed loop controller, which improves the power quality of the AC voltage and provides very low THD. In this work, 5% THD is considered, which is tolerable according to the Institute of Electrical and Electronics Engineers (IEEE) standard [
35].
The proposed work covers many of the shortcomings mentioned in the introduction section. However, to distinguish the proposed research from the previously published literature, the main contributions of this proposed work are summarized as follows:
Design of fuzzy logic based MPPT, which can track the continuous power without oscillations and noise near the maximum point.
Implementation of a push pull current-fed boost chopper in which high frequency transformer is used to provide the galvanic isolation between input and output, along with a high conversion ratio.
Implementation of a voltage source inverter (VSI) with a fuzzy logic closed loop controller, which improves the power quality of the AC voltage and provides very low THD.
Applications of two fuzzy logic controllers (FLCs) are employed in the proposed system and each has its unique fuzzy rule. The first one tracks the MPPT, and the second is used in VSI with a proper designed low pass filter to reduce the THD value.
Furthermore, a comparative section is added at the end, which is based on the literature on the fuzzy logic principle. The comparison includes the methodology used with fuzzy logic, implementation complexity, generalization in terms of symmetrical and asymmetrical membership function, inputs to the membership functions, hardware implementation, noise and oscillations near MPP, THD analysis, and the proper filter design. This comparative analysis also distinguishes the proposed research from the previously published literature. As a result, the proposed work has advantages in term of simple, accurate, and faster convergence to the operating point with minimum noise and THD levels.
This paper is organized as follows. An equivalent circuit for an individual PV cell based on a single diode model is presented in
Section 2 that serves as a basis for MPPT and defines the proposed methodology. The push pull converter and its design aspects are explained in
Section 3, while the fuzzy logic based MPPT algorithm is described in
Section 4. Furthermore, the push pull converter, VSI, and low pass filter design are explained in
Section 4. Simulation results and discussions are in
Section 5 and, finally, the conclusions are drawn in
Section 6.
3. Push Pull Converter and Its Design Aspects
The push pull converter consists of a centrally tapped transformer, two push pull switches
Q1 and
Q2, series inductor
L, two rectifier diodes
D1 and
D2, and a parallel capacitor
C0, as shown in
Figure 7. Push pull converter can operate in four states. By applying PWM, the designed inductor and specifications of load ensure that the converter always operates in the continuous conduction mode (CCM) [
36,
42].
A designed current fed DC–DC push pull boost converter is shown in
Figure 7 and its operating characteristics are given in
Table 2.
This circuit consists of a center tap transformer, two push pull switches Q1 and Q2, series inductor L, two rectifier diodes D1 and D2, and a parallel capacitor with the output load.
The turn ratio of the transformer is calculated as follows:
The switches’ on and off time are selected as follows:
When both switches are in an off state, the voltage across these switches is double. This voltage stress is compensated by selecting a transformer tapping voltage as follows:
During the dead time, when both switches are in the off position, the inductor increases in a linear mode of operation.
When only one switch is on, the energy is transferred to the secondary side of the transformer, then
The input current of the inductor with efficiency
is given by the following:
The inductor size is selected carefully, a very value inductor may cause the converter to operate in the discontinuous mode and very high-value inductor may cause an increase in the size and weight of the converter.
where
for optimal operation.
The proposed converter operates in CCM; therefore, minimum inductance is required at the output side, which is calculated as represented in Equation (18).
To operate the converter in CCM, the value of the output side capacitor is calculated as shown in Equation (19).
where
Vr is DC ripple voltage, which is 3% allowable.
The proposed converter topology offers the benefit of isolation between the input and output side, maximum efficiency, constant input current, high voltage conversion, the minimum number of switches, simplicity of configuration, and thus low conduction losses. Furthermore, there is no need for a filter capacitor at the input side that makes the system simple and compact [
34].
4. VSI and Low Pass Filter Design
In the proposed system, a single-phase full bridge inverter is used to feed the consumer load, which inverts the 340 V DC into 220 V AC at 50 Hz frequency. The unipolar sinusoidal pulse width modulation (USPWM) technique is used to turn on/off the inverter switches. This technique reduces THD and power losses during switching [
26]. In USPWM, two control signals are used: a sinusoidal wave and its 180° out of phase version at 50 Hz. These control signals are compared with high frequency triangular carrier signal of 10 kHz. Control signal 1 is compared with the carrier signal, resulting in a logic signal that generates the output voltage between 0 and +
Vdc. Control signal 2 is compared with the carrier signal, resulting in a logic signal that produces the output voltage between 0 and +
Vdc. In every inverter, a filter is necessary for improving power quality. Therefore, a low pass filter is used for smoothing the output current from VSI. The LCL filter is shown in
Figure 8.
The components of the filter are obtained as represented in Equations (20)–(25).
The filter value refers to base impedence
where
Zb is the base impedance and
V0 and
Pn are the output voltage and power of the inverter, respectively. The maximum power variation is considered as 5% and the base impedance is adjusted as computed in Equation (21):
where
L1 is inductance on inverter side. For 10% ripple,
L1 is calculated by considering the rated current of the inductor.
Further,
Imax is calculated as shown in Equation (23):
L1 is calculated as represented in Equation (24):
where
fs is switching frequency and
L2 is calculated as follows:
where
Ka is the attenuation factor and is taken as
Ka = 0.2, while
ωs = 2πf
s is angular switching frequency [
41,
43].
For LCL filter, derived parameters are
L1 = 9.3 mH,
L2 = 37.5 mH, and
C = 1.6 μF with the unity power factor. The LCL filter designing algorithm is shown in
Figure 9. After the filtration, (root mean square) RMS output voltages are sensed for the fuzzy logic controller input. The output of the controller is compared with the sinusoidal AC voltages at the fundamental frequency, and then PWM set the duty of VSI.
The fuzzy logic structure of VSI is the same as the MPP push pull converter; however, the fuzzy rules used in FLC of VSI are listed in
Table 3.
5. Results and Discussion
The proposed isolated photovoltaic system with a fuzzy logic controller, the current fed push pull DC–DC boost converter, is presented here. The DC–DC boost converter operates in continuous conduction mode, and the voltage source inverter with fuzzy logic closed loop and low pass filter are simulated in Matlab/Simulink. The parameters of the employed PV array (Canadian solar CS5P 250-M) are given in
Table 4. The performance of the developed system is tested at different irradiance intensity at 25 °C and a linear load of 200 Ω, as shown in
Figure 10. Voltage and current are sensed to calculate the power.
In the first case, the system is simulated at a constant temperature of 25 °C and constant irradiance 1000 W/m
2. It tracks the maximum power 250 W in a very small amount of time, approximately 0.005 s, as shown in
Figure 11.
In the second case, the system is simulated for various irradiance levels as follows: 800 W/m
2 for 0.015 s, 600 W/m
2 for 0.03 s, and 1000 W/m
2 for the rest of the time. In this scenario, it again tracks the maximum power point within the same designed spam of time (0.005 s) and gives the power of 200 W, 150 W, and 250 W, respectively, as shown in
Figure 12. This power is tracked through the fuzzy logic controller, where fuzzy rules are employed as given in
Table 1.
On the basis of the designed fuzzy rules, the fuzzy logic controllers generate the fuzzy logic PWM and decide what would be the duty ratio of the push pull boost converter switches shown in
Figure 13a. The push pull boost converter operates in four states. In state 1, when the switch
Q1 is on, then the inductor would discharge, and output voltage would be positive. In state 2, switches
Q1 and
Q2 are ON simultaneously. During state 2, the inductor is charged, as shown in
Figure 13c, that is, 4.3 A current flows, and the output voltage would be zero because the flux generated in both windings cancels each other out. In this state, the output capacitor provides the voltage to the load, which means that the capacitor would be discharged. In state 3, when the switch
Q2 is ON, then the voltage would be negative. Similarly, in state 4, both switches are ON simultaneously for zero output voltages, as shown in
Figure 13b, that is, modified sine wave voltages, which are converted into 340 V DC through the rectifier diodes
D1 and
D2, as shown in
Figure 13d.
The DC voltages are inverted into 220 V AC through the VSI. These voltages are sensed through the voltage sensor and compared with the sinusoidal AC voltages; error and change in error are calculated for fuzzy FLC. This controller generates the reference signal for the PWM generator, which operates the inverter switches and is operated according to the fuzzy logic controller. The PWM of the inverter is shown in
Figure 14a. FLC generates the reference signal by using the fuzzy rules of
Table 3, settled down by removing the lower order harmonic content in AC voltages. However, these voltages still have the higher-order harmonic content shown in
Figure 14b. The higher-order harmonic contents are removed through the low pass filter. The output voltages and current of inverter after removing the higher-order harmonic content are shown in
Figure 15, where
Figure 15a presents the AC voltages and
Figure 15b shows the AC current, which is 1.5 A.
To check the quality of output voltages and current, the fast Fourier transform (FFT) analysis is also carried out and obtained THD are shown in
Figure 16 and
Figure 17. The FFT analysis of the proposed algorithm provides only 1.41% THD for output voltage and current at 50 Hz.
To prove the validity of the conducted research, a comparison between the results of the fuzzy logic-based MPPT algorithm is compared with P&O and incremental conductance algorithms available in the literature, in the time domain function at irradiance 1000 W/m
2 and at 25 °C, as listed in
Table 5.
Comparative Analysis
In this section, a comparative analysis is performed, which is represented in
Table 6. The comparative analysis is based on the literature on the fuzzy logic principle. The comparison includes the methodology used with fuzzy logic, implementation complexity, generalization in terms of symmetrical and asymmetrical membership function, inputs to the membership functions, hardware implementation, noise and oscillations near MPP, THD analysis, and the proper filter design. This comparative analysis also distinguishes the proposed research from the previous published literature. As a result, the proposed work has advantages in terms of simple, accurate, and faster convergence to the operating point with minimum noise and THD.